On cherche à étudier l’effet de trois facteurs sur le transcriptome des racines d’Arabidopsis thaliana et de la micro Tomate.

## Warning in scan(file = file, what = what, sep = sep, quote = quote, dec = dec, :
## EOF within quoted string
## Warning in scan(file = file, what = what, sep = sep, quote = quote, dec = dec, :
## number of items read is not a multiple of the number of columns

CO2

Clustering

****************************************
coseq analysis: Poisson approach & none transformation
K = 2 to 12 
Use set.seed() prior to running coseq for reproducible results.
****************************************
Running g = 2 ...
[1] "Initialization: 1"
[1] "Log-like diff: 0"
Running g = 3 ...
[1] "Initialization: 1"
[1] "Log-like diff: 0"
Running g = 4 ...
[1] "Initialization: 1"
[1] "Log-like diff: 9.85664883046411e-11"
Running g = 5 ...
[1] "Initialization: 1"
[1] "Log-like diff: 2.8421709430404e-14"
Running g = 6 ...
[1] "Initialization: 1"
[1] "Log-like diff: 0"
Running g = 7 ...
[1] "Initialization: 1"
[1] "Log-like diff: 5.98119243022666e-08"
Running g = 8 ...
[1] "Initialization: 1"
[1] "Log-like diff: 0"
Running g = 9 ...
[1] "Initialization: 1"
[1] "Log-like diff: 0"
Running g = 10 ...
[1] "Initialization: 1"
[1] "Log-like diff: 8.95090579433599e-09"
Running g = 11 ...
[1] "Initialization: 1"
[1] "Log-like diff: 3.56862983608153e-10"
Running g = 12 ...
[1] "Initialization: 1"
[1] "Log-like diff: 0"
$ICL


$profiles


$boxplots


$probapost_barplots


*************************************************
Model: Poisson
Transformation: none
*************************************************
Clusters fit: 2,3,4,5,6,7,8,9,10,11,12
Clusters with errors: ---
Selected number of clusters via ICL: 12
ICL of selected model: -740101.6
*************************************************
Number of clusters = 12
ICL = -740101.6
*************************************************
Cluster sizes:
 Cluster 1  Cluster 2  Cluster 3  Cluster 4  Cluster 5  Cluster 6  Cluster 7 
         4          2         15          9          6         12         12 
 Cluster 8  Cluster 9 Cluster 10 Cluster 11 Cluster 12 
         9         22         13         18          9 

Number of observations with MAP > 0.90 (% of total):
131 (100%)

Number of observations with MAP > 0.90 per cluster (% of total per cluster):
 Cluster 1 Cluster 2 Cluster 3 Cluster 4 Cluster 5 Cluster 6 Cluster 7
 4         2         15        9         6         12        12       
 (100%)    (100%)    (100%)    (100%)    (100%)    (100%)    (100%)   
 Cluster 8 Cluster 9 Cluster 10 Cluster 11 Cluster 12
 9         22        13         18         9         
 (100%)    (100%)    (100%)     (100%)     (100%)    

Visualisation en ACP

Class: pca dudi
Call: dudi.pca(df = log(data + 0.1), center = TRUE, scale = TRUE, scannf = FALSE, 
    nf = 4)

Total inertia: 24

Eigenvalues:
    Ax1     Ax2     Ax3     Ax4     Ax5 
17.2837  4.7082  0.6320  0.5028  0.2617 

Projected inertia (%):
    Ax1     Ax2     Ax3     Ax4     Ax5 
 72.015  19.617   2.633   2.095   1.090 

Cumulative projected inertia (%):
    Ax1   Ax1:2   Ax1:3   Ax1:4   Ax1:5 
  72.02   91.63   94.27   96.36   97.45 

(Only 5 dimensions (out of 24) are shown)

NULL

Réseau

Nitrate

****************************************
coseq analysis: Poisson approach & none transformation
K = 2 to 12 
Use set.seed() prior to running coseq for reproducible results.
****************************************
Running g = 2 ...
[1] "Initialization: 1"
[1] "Log-like diff: 4.59017712728382e-10"
Running g = 3 ...
[1] "Initialization: 1"
[1] "Log-like diff: 0.0273270790755795"
[1] "Log-like diff: 0.00026883443707959"
[1] "Log-like diff: 3.19062152343008e-06"
Running g = 4 ...
[1] "Initialization: 1"
[1] "Log-like diff: 0.00317565071132719"
[1] "Log-like diff: 6.45337852631656e-05"
[1] "Log-like diff: 1.32643063111004e-06"
Running g = 5 ...
[1] "Initialization: 1"
[1] "Log-like diff: 2.50261926737494e-08"
Running g = 6 ...
[1] "Initialization: 1"
[1] "Log-like diff: 0.11469606455513"
[1] "Log-like diff: 0.025426511331073"
[1] "Log-like diff: 0.00543256043100371"
[1] "Log-like diff: 0.00115565145672747"
[1] "Log-like diff: 0.000245569560078707"
Running g = 7 ...
[1] "Initialization: 1"
[1] "Log-like diff: 3.85815361880759e-08"
Running g = 8 ...
[1] "Initialization: 1"
[1] "Log-like diff: 0.0269953964198315"
[1] "Log-like diff: 0.00258195816950035"
[1] "Log-like diff: 0.000242992206885617"
[1] "Log-like diff: 2.48859676972302e-05"
[1] "Log-like diff: 2.3691358670419e-06"
Running g = 9 ...
[1] "Initialization: 1"
[1] "Log-like diff: 1.1980061351835e-06"
Running g = 10 ...
[1] "Initialization: 1"
[1] "Log-like diff: 8.60689688408911e-08"
Running g = 11 ...
[1] "Initialization: 1"
[1] "Log-like diff: 12.0894854576141"
[1] "Log-like diff: 1.04941717138947"
[1] "Log-like diff: 0.652127854852324"
[1] "Log-like diff: 0.302500253305844"
[1] "Log-like diff: 0.144134278076002"
Running g = 12 ...
[1] "Initialization: 1"
[1] "Log-like diff: 3.23944817637312e-08"
$ICL


$profiles


$boxplots


$probapost_barplots


*************************************************
Model: Poisson
Transformation: none
*************************************************
Clusters fit: 2,3,4,5,6,7,8,9,10,11,12
Clusters with errors: ---
Selected number of clusters via ICL: 12
ICL of selected model: -3013888
*************************************************
Number of clusters = 12
ICL = -3013888
*************************************************
Cluster sizes:
 Cluster 1  Cluster 2  Cluster 3  Cluster 4  Cluster 5  Cluster 6  Cluster 7 
        63         23         63        164         53          7         40 
 Cluster 8  Cluster 9 Cluster 10 Cluster 11 Cluster 12 
       107         73        121         19        104 

Number of observations with MAP > 0.90 (% of total):
837 (100%)

Number of observations with MAP > 0.90 per cluster (% of total per cluster):
 Cluster 1 Cluster 2 Cluster 3 Cluster 4 Cluster 5 Cluster 6 Cluster 7
 63        23        63        164       53        7         40       
 (100%)    (100%)    (100%)    (100%)    (100%)    (100%)    (100%)   
 Cluster 8 Cluster 9 Cluster 10 Cluster 11 Cluster 12
 107       73        121        19         104       
 (100%)    (100%)    (100%)     (100%)     (100%)    
Class: pca dudi
Call: dudi.pca(df = log(data + 0.1), center = TRUE, scale = TRUE, scannf = FALSE, 
    nf = 4)

Total inertia: 24

Eigenvalues:
    Ax1     Ax2     Ax3     Ax4     Ax5 
19.1693  3.3531  0.5281  0.3930  0.1205 

Projected inertia (%):
    Ax1     Ax2     Ax3     Ax4     Ax5 
 79.872  13.971   2.200   1.638   0.502 

Cumulative projected inertia (%):
    Ax1   Ax1:2   Ax1:3   Ax1:4   Ax1:5 
  79.87   93.84   96.04   97.68   98.18 

(Only 5 dimensions (out of 24) are shown)

NULL

Iron

****************************************
coseq analysis: Poisson approach & none transformation
K = 2 to 12 
Use set.seed() prior to running coseq for reproducible results.
****************************************
Running g = 2 ...
[1] "Initialization: 1"
[1] "Log-like diff: 1.06819442180495e-10"
Running g = 3 ...
[1] "Initialization: 1"
[1] "Log-like diff: 0.0758628365212921"
[1] "Log-like diff: 0.0198623147528725"
[1] "Log-like diff: 0.00525704057190701"
[1] "Log-like diff: 0.00139919104513453"
[1] "Log-like diff: 0.000341532015648127"
Running g = 4 ...
[1] "Initialization: 1"
[1] "Log-like diff: 1.07776350178478"
[1] "Log-like diff: 0.861659587475172"
[1] "Log-like diff: 0.726989034887513"
[1] "Log-like diff: 0.605308819941396"
[1] "Log-like diff: 0.565365021112715"
Running g = 5 ...
[1] "Initialization: 1"
[1] "Log-like diff: 0.0387118669603943"
[1] "Log-like diff: 0.00732243729624926"
[1] "Log-like diff: 0.00136628427148366"
[1] "Log-like diff: 0.000246221735089591"
[1] "Log-like diff: 4.5249045658835e-05"
Running g = 6 ...
[1] "Initialization: 1"
[1] "Log-like diff: 982.457065455328"
[1] "Log-like diff: 1519.26082567653"
[1] "Log-like diff: 1441.62668682535"
[1] "Log-like diff: 646.824080182582"
[1] "Log-like diff: 912.054075951454"
Running g = 7 ...
[1] "Initialization: 1"
[1] "Log-like diff: 3075.61250726608"
[1] "Log-like diff: 4564.13076702637"
[1] "Log-like diff: 3258.49420404661"
[1] "Log-like diff: 427.579259682921"
[1] "Log-like diff: 812.618604827518"
Running g = 8 ...
[1] "Initialization: 1"
[1] "Log-like diff: 880.31287483351"
[1] "Log-like diff: 275.32058591329"
[1] "Log-like diff: 326.646768080913"
[1] "Log-like diff: 72.2925259231334"
[1] "Log-like diff: 302.279208953787"
Running g = 9 ...
[1] "Initialization: 1"
[1] "Log-like diff: 517.306169679352"
[1] "Log-like diff: 389.248458924899"
[1] "Log-like diff: 457.434804311791"
[1] "Log-like diff: 55.4858077387178"
[1] "Log-like diff: 317.507489438035"
Running g = 10 ...
[1] "Initialization: 1"
[1] "Log-like diff: 4903.28126139813"
[1] "Log-like diff: 305.975433627477"
[1] "Log-like diff: 144.421057044739"
[1] "Log-like diff: 318.02902505084"
[1] "Log-like diff: 217.090435952164"
Running g = 11 ...
[1] "Initialization: 1"
[1] "Log-like diff: 541.75667588397"
[1] "Log-like diff: 525.656228746016"
[1] "Log-like diff: 258.144348521114"
[1] "Log-like diff: 161.017591467345"
[1] "Log-like diff: 11.9961394017322"
Running g = 12 ...
[1] "Initialization: 1"
[1] "Log-like diff: 80.4584674238448"
[1] "Log-like diff: 103.550361538123"
[1] "Log-like diff: 156.229544983786"
[1] "Log-like diff: 136.7482838086"
[1] "Log-like diff: 240.690850675943"
$ICL


$profiles


$boxplots


$probapost_barplots


*************************************************
Model: Poisson
Transformation: none
*************************************************
Clusters fit: 2,3,4,5,6,7,8,9,10,11,12
Clusters with errors: ---
Selected number of clusters via ICL: 12
ICL of selected model: -3391000
*************************************************
Number of clusters = 12
ICL = -3391000
*************************************************
Cluster sizes:
 Cluster 1  Cluster 2  Cluster 3  Cluster 4  Cluster 5  Cluster 6  Cluster 7 
       352         22         34         36        238        166        235 
 Cluster 8  Cluster 9 Cluster 10 Cluster 11 Cluster 12 
        69        506        747        173        263 

Number of observations with MAP > 0.90 (% of total):
2748 (96.73%)

Number of observations with MAP > 0.90 per cluster (% of total per cluster):
 Cluster 1 Cluster 2 Cluster 3 Cluster 4 Cluster 5 Cluster 6 Cluster 7
 344       22        34        34        222       156       220      
 (97.73%)  (100%)    (100%)    (94.44%)  (93.28%)  (93.98%)  (93.62%) 
 Cluster 8 Cluster 9 Cluster 10 Cluster 11 Cluster 12
 62        499       735        173        247       
 (89.86%)  (98.62%)  (98.39%)   (100%)     (93.92%)  
Class: pca dudi
Call: dudi.pca(df = log(data + 0.1), center = TRUE, scale = TRUE, scannf = FALSE, 
    nf = 4)

Total inertia: 24

Eigenvalues:
     Ax1      Ax2      Ax3      Ax4      Ax5 
22.03089  1.11520  0.27107  0.11321  0.06945 

Projected inertia (%):
    Ax1     Ax2     Ax3     Ax4     Ax5 
91.7954  4.6467  1.1295  0.4717  0.2894 

Cumulative projected inertia (%):
    Ax1   Ax1:2   Ax1:3   Ax1:4   Ax1:5 
  91.80   96.44   97.57   98.04   98.33 

(Only 5 dimensions (out of 24) are shown)

NULL

 

A work by Océane Cassan

oceane.cassan@supagro.fr